Calculate Row Variances

This function calculates the sample variance for each row of a numeric matrix or data frame. It uses the standard sample variance formula with Bessel's correction (dividing by n-1).

Usage

rowVars(x, na.rm = TRUE)

Arguments

x

A numeric matrix or data frame for which row variances are to be calculated.

na.rm

A logical value indicating whether missing values (NA) should be removed before calculating variances. If TRUE (default), missing values are excluded and the denominator is adjusted accordingly. If FALSE, any row containing missing values will result in NA.

Value

A numeric vector of length equal to the number of rows in x, containing the sample variance for each row.

Details

The function computes sample variances using the formula: $$\sigma^2 = \frac{\sum_{i=1}^{n}(x_i - \bar{x})^2}{n-1}$$

When na.rm = TRUE, the function:

• Excludes missing values from mean and variance calculations

• Adjusts the degrees of freedom (n-1) based on the number of non-missing values in each row

• Returns NaN for rows with fewer than 2 non-missing values

When na.rm = FALSE, the function:

• Uses all values including missing ones

• Returns NA for any row containing missing values

• Uses ncol(x) - 1 as the degrees of freedom

See also

var for column-wise variance calculation, rowMeans for row means, apply for applying functions across rows or columns

Examples

if (FALSE) { # \dontrun{
# Basic usage with a matrix
mat <- matrix(1:12, nrow = 3)
rowVars(mat)

# With missing values
mat[1, 2] <- NA
mat[2, 3] <- NA
rowVars(mat, na.rm = TRUE)   # Excludes NAs
rowVars(mat, na.rm = FALSE)  # Includes NAs (returns NA for affected rows)

# With a data frame
df <- data.frame(
  a = c(1, 4, 7),
  b = c(2, 5, 8),
  c = c(3, 6, 9)
)
rowVars(df)

# Edge case: single column (variance is 0)
single_col <- matrix(1:3, ncol = 1)
rowVars(single_col)  # Returns NaN due to division by 0
} # }